Characteristics of locational uncertainty marginal price for correlated uncertainties of variable renewable generation and demands. (15th January 2021) - Fang, Xin; Cui, Hantao; Du, Ershun; Li, Fangxing; Kang, Chongqing
Highlights: A scenario-free stochastic market model dispatches the generation considering uncertainty. A locational uncertainty marginal price prices the uncertainty of variable generation and load. The correlation of variable generation and load is modeled in the economic dispatch and price formation. The revenue adequacy and market equilibrium are analyzed. The generation cost recovery is maintained considering the uncertainty. Abstract: With the rapid increase of variable renewable energy sources in power systems, how to manage and price the uncertainty of renewable resources' power outputs is becoming an urgent issue. Current market designs considering the uncertainties are mainly based on the probabilistic scenario set of demand and renewable energy resources power outputs. This consideration makes market designs vulnerable to three significant challenges when put into practice. First, the accurate probability distribution of renewable generation is hard to obtain in real-time. Second, it is challenging to clear the market timely with many scenarios to guarantee accuracy. Third, generation cost recovery cannot be guaranteed for some scenarios. To overcome these challenges, this paper proposes a locational uncertainty marginal price model to price the uncertainty explicitly based on a scenario-free stochastic market-clearing model. Instead of using the probabilistic scenario set, the uncertainty of renewable energy sources and loads is modeled with distributionally-robust chance constraints. The correlation of uncertainties can be endogenously modeled in both the market-clearing and the locational uncertainty marginal price formation. Furthermore, this paper proves that generation cost recovery, revenue adequacy, and partial market equilibrium can be achieved using the locational uncertainty marginal price model. Numerical results from both the small and large systems simulations validate that the generation cost recovery is maintained no matter the generation participates in uncertainty mitigation or not. The transmission congestion surplus is also allocated appropriately among loads, renewable energy sources, and financial transmission right owners.